Autonomous Truck Navigation With Trailer Integration Via Natural Language Processing

Junseo(Jason) Kim, Rishabh Raja, Ateeb Jawaid, and Raymond Ha

Toronto Metropolitan University (TMU)

Autonomous Truck Navigation With Trailer Integration Via Natural Language Processing

This paper presents an innovative approach to autonomous truck and trailer navigation, integrating Natural Language Processing (NLP) with advanced sensor fusion technologies to facilitate human-vehicle interaction. The system achieves precise controls and adaptability across various environmental contexts by leveraging the potential of Lidar, Inertial Measurement Units (IMU), and an Ackerman steering mechanism. Finite Element Analysis (FEA) presents structural integrity and operational efficiency, ensuring the system’s robustness. Emphasizing a comprehensive development strategy, this study bridges mechanical engineering and computational intelligence, highlighting NLP’s pivotal role in enhancing navigational commands and decision-making processes. We discuss multidisciplinary methodology, design rationale, and the integration of cutting-edge technologies that promise a user-friendly autonomous driving experience. 

What is the Project about

The project’s core innovation lies in the application of NLP for interpreting user commands for navigation, simplifying human-machine interactions significantly. NLP allows for an intuitive interface where operators can direct the autonomous system using natural language, which significantly enhances the traditional manual or predefined command inputs. The system demonstrates precise maneuverability and adaptability to diverse environmental conditions, coupled with an Ackerman steering-based robotic platform and integrated sensor technologies, including Lidar and Inertial Measurement Units (IMU). The main contribution of this work is the development of a robust mechanical and computational framework that synergizes hardware robustness with software intelligence. Through comprehensive Finite Element Analysis (FEA), the structural integrity and reliability of the truck and trailer system are validated, ensuring its durability and efficiency. The integration of mechanical precision with advanced sensor fusion and NLP technologies provides insight into the future of autonomous transportation.